- talks - lists.cs.princeton.edu

The "D" word: solving for "diversity" on high-tech teams, tomorrow at 4:30pm
by Emily C. Lawrence 27 Feb '19

27 Feb '19

The “D” word: solving for “diversity” on high-tech teams Janet Vertesi, Assistant Professor of Sociology at Princeton University Thursday, February 28th at 4:30 PM Bowen Hall 222 This talk gives an overview of the sociological factors that affect the construction of diverse, high-performing teams. Building on a summary of key issues that affect gender and racial disparities in high-performing occupations, the talk covers the current social science and vocabulary for addressing the problem, as well as strategies for moving forward, avoiding common traps, and protecting performance-based advancement. Bio: Dubbed “Margaret Mead among the Starfleet” by the Times Literary Supplement, Janet Vertesi is Assistant Professor of Sociology at Princeton University and an expert in the sociology of science, technology, and organizations. Vertesi’s past decade of research, funded by the National Science Foundation, examines how distributed robotic spacecraft teams work together effectively to produce scientific and technical results. Her book Seeing Like a Rover (University of Chicago Press, 2015) describes the collaborative work of the Mars Exploration Rover mission including the people, the images, and the robots who do science on Mars. Vertesi is also a long-time contributor to the Association of Computing Machinery conferences on human-computer interaction and computer-supported cooperative work. She is an advisory board member of the Data and Society institute in New York City and is a member of Princeton University’s Center for Information Technology Policy. To join the talk, please email [ mailto:seasdiversity@princeton.edu | seasdiversity(a)princeton.edu ] . This event co-sponsored by School of Engineering and Applied Science and Department of Computer Science.

1 0

Colloquium Speaker-Tianqi Chen, 3/5 at 12:30pm
by Emily C. Lawrence 27 Feb '19

27 Feb '19

Colloquium Speaker Tianqi Chen from the University of Washington Tuesday, March 5 - 12:30pm Computer Science - Room 105 Host: Ryan Adams [ http://www.cs.princeton.edu/events/25820 | http://www.cs.princeton.edu/events/25820 ] Learning-based Learning Systems Data, models, and computing are the three pillars that enable machine learning to solve real-world problems at scale. Making progress on these three domains requires not only disruptive algorithmic advances but also systems innovations that can continue to squeeze more efficiency out of modern hardware. Learning systems are in the center of every intelligent application nowadays. However, the ever-growing demand for applications and hardware specialization creates a huge engineering burden for these systems, most of which rely on heuristics or manual optimization. In this talk, I will present a new approach that uses machine learning to automate system optimizations. I will describe our approach in the context of deep learning deployment problem. I will first discuss how to design invariant representations that can lead to transferable statistical cost models, and apply these representations to optimize tensor programs used in deep learning applications. I will then describe the system improvements we made to enable diverse hardware backends . TVM, our end-to-end system, delivers performance across hardware back-ends that are competitive with state-of-the-art, hand-tuned deep learning frameworks. Finally, I will discuss how to generalize our approach to do full-stack optimization of the model, system, hardware jointly, and how to build systems to support life-long evolution of intelligent applications. Bio: Tianqi Chen is a Ph.D. candidate in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, working with Carlos Guestrin on the intersection of machine learning and systems. He has created three major learning systems that are widely adopted: XGBoost , TVM, and MXNet (co-creator). He is a recipient of the Google Ph.D. Fellowship in Machine Learning.

1 0

EE and CS Speaker-Jaime Fernández Fisac, TODAY at 4:30pm
by Emily C. Lawrence 26 Feb '19

26 Feb '19

EE and CS Speaker Jaime Fernández Fisac from the University of California, Berkeley Tuesday, February 26 - 4:30pm Computer Science 105 Hosts: Naveen Verma , EE and Szymon Rusinkiewicz, CS [ http://ee.princeton.edu/events/resilient-safety-assurance-robotic-systems-s… | http://ee.princeton.edu/events/resilient-safety-assurance-robotic-systems-s… ] Resilient Safety Assurance for Robotic Systems: Staying Safe Even When Models Are Wrong In order for autonomous systems like robots, drones, and self-driving cars to be reliably introduced into our society, they must be able to actively account for safety during their operation. While safety analysis has traditionally been conducted offline for controlled environments like cages on factory floors, the much higher complexity of open, human-populated spaces like our homes, cities, and roads means that any precomputed guarantees may become invalid when modeling assumptions made at design time are violated once the system is deployed. My research aims to enable autonomous systems to proactively ensure safety during their operation by explicitly reasoning about the gap between their models and the real world. In this talk I will present recent contributions to safety assurance for autonomous systems. I will first discuss new advances in efficient safety computation, and demonstrate their use in large-scale unmanned air traffic. Next, I will present a general safety framework enabling the use of learning control schemes (e.g. reinforcement learning) for safety-critical robotic systems in uncertain environments. I will then turn my attention to the important problem of safe human-robot interaction, and introduce a real-time Bayesian method to monitor the reliability of predictive human models. The talk will end with a discussion of challenges and opportunities ahead, including the introduction of game-theoretic planning in autonomous driving and the bridging of safety analysis and deep reinforcement learning. Bio: Jaime Fernández Fisac is a Ph.D. candidate in Electrical Engineering and Computer Sciences at UC Berkeley. His research interests lie in control theory and artificial intelligence, with a focus on safety assurance for autonomous systems. He works to enable robotic systems to safely interact with uncertain environments and human beings despite using inaccurate models. Jaime received a B.S./M.S. degree in Electrical Engineering from the Universidad Politécnica de Madrid, Spain, in 2012, and a M.Sc. in Aeronautics from Cranfield University, U.K., in 2013. He is a recipient of the La Caixa Foundation fellowship.

1 0

Reminder: EE and CS Speaker-Jaime Fernández Fisac, tomorrow at 4:30pm
by Emily C. Lawrence 25 Feb '19

25 Feb '19

EE and CS Speaker Jaime Fernández Fisac from the University of California, Berkeley Tuesday, February 26 - 4:30pm Computer Science 105 Hosts: Naveen Verma, EE and Szymon Rusinkiewicz, CS [ http://ee.princeton.edu/events/resilient-safety-assurance-robotic-systems-s… | http://ee.princeton.edu/events/resilient-safety-assurance-robotic-systems-s… ] Resilient Safety Assurance for Robotic Systems: Staying Safe Even When Models Are Wrong In order for autonomous systems like robots, drones, and self-driving cars to be reliably introduced into our society, they must be able to actively account for safety during their operation. While safety analysis has traditionally been conducted offline for controlled environments like cages on factory floors, the much higher complexity of open, human-populated spaces like our homes, cities, and roads means that any precomputed guarantees may become invalid when modeling assumptions made at design time are violated once the system is deployed. My research aims to enable autonomous systems to proactively ensure safety during their operation by explicitly reasoning about the gap between their models and the real world. In this talk I will present recent contributions to safety assurance for autonomous systems. I will first discuss new advances in efficient safety computation, and demonstrate their use in large-scale unmanned air traffic. Next, I will present a general safety framework enabling the use of learning control schemes (e.g. reinforcement learning) for safety-critical robotic systems in uncertain environments. I will then turn my attention to the important problem of safe human-robot interaction, and introduce a real-time Bayesian method to monitor the reliability of predictive human models. The talk will end with a discussion of challenges and opportunities ahead, including the introduction of game-theoretic planning in autonomous driving and the bridging of safety analysis and deep reinforcement learning. Bio: Jaime Fernández Fisac is a Ph.D. candidate in Electrical Engineering and Computer Sciences at UC Berkeley. His research interests lie in control theory and artificial intelligence, with a focus on safety assurance for autonomous systems. He works to enable robotic systems to safely interact with uncertain environments and human beings despite using inaccurate models. Jaime received a B.S./M.S. degree in Electrical Engineering from the Universidad Politécnica de Madrid, Spain, in 2012, and a M.Sc. in Aeronautics from Cranfield University, U.K., in 2013. He is a recipient of the La Caixa Foundation fellowship.

1 0

14th Annual Innovation Forum
by Emily Lawrence 22 Feb '19

22 Feb '19

The 14th Annual Innovation Forum is fast approaching. The forum will take place on Wednesday, February 27th at 2:00pm in the Andlinger Center's Meader Hall at Princeton University. This event will showcase Princeton research developed by faculty, postdoctoral researchers and graduate students that offers the potential to be commercialized. Nine teams will be vying for prize money totally $30,000 to bring their innovations to market. Find out who is in the running here <https://kellercenter.princeton.edu/engage/innovation-forum/teams> and register here <https://princeton.us8.list-manage.com/track/click?u=7fbf3b0a07b0fd95cded6d6 03&id=41a8afb45c&e=44e890d167> to save your seat for this exciting chance to see the newest innovations emerging from Princeton University labs. Hosted by the Keller Center <https://kellercenter.princeton.edu/> and the Office of Technology Licensing <https://patents.princeton.edu/> Beth Ellen Jarvie Communication Coordinator Keller Center School of Engineering and Appied Science Princeton University ACE 25 Engineering Quadrangle Princeton, NJ 08544 609-258-1643

1 0

Andy Zeng will present his Pre FPO "Visual Affordances for Robotic Manipulation" on Tuesday, February 26, 2019 at 10am in CS 401
by Nicki Mahler 22 Feb '19

22 Feb '19

Andy Zeng will present his Pre FPO "Visual Affordances for Robotic Manipulation" on Tuesday, February 26, 2019 at 10am in CS 401. The members of his committee are as follows: Tom Funkhouser (adviser), Szymon Rusinkiewicz, Olga Russakovsky, Adam Finkelstein, Alberto Rodriguez (MIT). All are welcome to attend. Please see below for talk title and abstract. Title: Learning Visual Affordances for Robotic Manipulation Abstract: How do we use machine learning algorithms to enable robots to intelligently interact with the physical world? On the one hand, we can adapt the classic sense-plan-act paradigm by replacing individual modules (e.g. pose estimation) with deep networks -- but these algorithms remain specific to training scenarios and suffer from error propagation. On the other hand, we can learn end-to-end models that map from raw sensory data (RGB images) to low-level control (motor torques) -- but these models are highly data inefficient, and robot data is expensive. In this talk, I will propose a middleground: learning the visual affordances of motion primitives. The idea is to use stable control algorithms to design motion primitives, then use deep learning to map from visual observations to the perceived affordances (e.g. confidence scores, or action-values) of these primitives. By abstracting away low-level control from the learning, this formulation significantly improves data efficiency, while exhibiting versatility and generalization. First, I will show how this formulation can be used to learn manipulation policies for multiple grasping primitives that generalize to new objects, enabling a real-world picking system to win 1st place (stow task) at the Amazon Robotics Challenge. Next, I will discuss how we can adapt this framework to enable long-term planning for sequencing manipulation primitives (e.g. pushing and grasping) with self-supervised deep reinforcement learning. This results in a system that learns to execute complex manipulation strategies that can generalize to new objects, and requires orders of magnitude less data than prior work. Lastly, I will talk about how we can augment this formulation by inferring additional control parameters -- in particular, by learning to predict residuals on top of parameter estimates from physics-based control. This enables the creation of TossingBot, a robot arm that is capable of grasping arbitrary objects and throwing them to target locations outside its maximum reach range. TossingBot throws more accurately than the average Google engineer, learns within a few hours via self-supervision, generalizes to new objects and locations, and achieves picking speeds twice as fast as state-of-the-art systems at 500+ mean picks per hour. In all settings, our experiments demonstrate that learning affordances for motion primitives can enable systems to quickly learn complex manipulation skills that scale to novel scenarios.

1 0

Colloquium Speaker Elissa Redmiles TODAY at 12:30pm
by Emily C. Lawrence 21 Feb '19

21 Feb '19

Colloquium Speaker Elissa Redmiles from University of Maryland Thursday, February 21 - 12:30pm Computer Science - Room 105 Host: Arvind Narayanan [ http://www.cs.princeton.edu/events/25817 | http://www.cs.princeton.edu/events/25817 ] Security for All: Modeling Structural Inequities to Design More Secure Systems Users often fall for phishing emails, reuse simple passwords, and fail to effectively utilize "provably" secure systems. These behaviors expose users to significant harm and frustrate industry practitioners and security researchers alike. As consequences of security breaches become ever more grave, it is important to study why humans behave seemingly irrationally. In this talk, I will illustrate how modeling the effects of structural inequities -- variance in skill, socioeconomic status, as well as culture and gender identity -- can both explain apparent irrationality in users’ security behavior and offer tangible improvements in industry systems. Modeling and mitigating security inequities requires a combination of techniques from economic, data scientific, and social science methodologies to develop new tools for systematically understanding and mitigating insecure behavior. Through novel experimental methodology, I empirically show strong evidence of bounded rationality in security behavior: Users make mathematically modelable tradeoffs between the protection offered by security behaviors and the costs of practicing those behaviors, which even in a highly usable system may outweigh the benefits, especially for less resourced users. These findings emphasize the need for industry systems that balance structural inequities and accommodate behavioral variance between users rather than one-size-fits-all security solutions. More broadly, my techniques for modeling and accounting for inequities have offered key insights in growing technical areas beyond security, including algorithmic fairness. Bio: Elissa Redmiles is a Ph.D. Candidate in Computer Science at the University of Maryland and has been a visiting researcher with the Max Planck Institute for Software Systems and the University of Zurich. Elissa’s research interests are broadly in the areas of security and privacy. She uses computational, economic, and social science methods to conduct research on behavioral security. Elissa seeks to understand users’ security and privacy decision-making processes and specifically investigate inequalities that arise in these processes and to mitigate those inequalities through the design of systems that facilitate safety equitably across users. Elissa is the recipient of a NSF Graduate Research Fellowship, a National Science Defense and Engineering Graduate Fellowship, and a Facebook Fellowship. Her work has appeared in popular press publications such as Scientific American, Business Insider, Newsweek, and CNET and has been recognized with the John Karat Usable Privacy and Security Student Research Award, a Distinguished Paper Award at USENIX Security 2018, and a University of Maryland Outstanding Graduate Student Award.

1 0

EE and CS Speaker-Jaime Fernández Fisac, 2/26 at 4:30pm
by Emily C. Lawrence 21 Feb '19

21 Feb '19

EE and CS Speaker Jaime Fernández Fisac from the University of California, Berkeley Tuesday, February 26 - 4:30pm Computer Science 105 Hosts: Naveen Verma , EE and Szymon Rusinkiewicz, CS [ http://ee.princeton.edu/events/resilient-safety-assurance-robotic-systems-s… | http://ee.princeton.edu/events/resilient-safety-assurance-robotic-systems-s… ] Resilient Safety Assurance for Robotic Systems: Staying Safe Even When Models Are Wrong In order for autonomous systems like robots, drones, and self-driving cars to be reliably introduced into our society, they must be able to actively account for safety during their operation. While safety analysis has traditionally been conducted offline for controlled environments like cages on factory floors, the much higher complexity of open, human-populated spaces like our homes, cities, and roads means that any precomputed guarantees may become invalid when modeling assumptions made at design time are violated once the system is deployed. My research aims to enable autonomous systems to proactively ensure safety during their operation by explicitly reasoning about the gap between their models and the real world. In this talk I will present recent contributions to safety assurance for autonomous systems. I will first discuss new advances in efficient safety computation, and demonstrate their use in large-scale unmanned air traffic. Next, I will present a general safety framework enabling the use of learning control schemes (e.g. reinforcement learning) for safety-critical robotic systems in uncertain environments. I will then turn my attention to the important problem of safe human-robot interaction, and introduce a real-time Bayesian method to monitor the reliability of predictive human models. The talk will end with a discussion of challenges and opportunities ahead, including the introduction of game-theoretic planning in autonomous driving and the bridging of safety analysis and deep reinforcement learning. Bio: Jaime Fernández Fisac is a Ph.D. candidate in Electrical Engineering and Computer Sciences at UC Berkeley. His research interests lie in control theory and artificial intelligence, with a focus on safety assurance for autonomous systems. He works to enable robotic systems to safely interact with uncertain environments and human beings despite using inaccurate models. Jaime received a B.S./M.S. degree in Electrical Engineering from the Universidad Politécnica de Madrid, Spain, in 2012, and a M.Sc. in Aeronautics from Cranfield University, U.K., in 2013. He is a recipient of the La Caixa Foundation fellowship.

1 0

Reminder: Colloquium Speaker Elissa Redmiles tomorrow at 12:30pm
by Emily C. Lawrence 20 Feb '19

20 Feb '19

Colloquium Speaker Elissa Redmiles from University of Maryland Thursday, February 21 - 12:30pm Computer Science - Room 105 Host: Arvind Narayanan [ http://www.cs.princeton.edu/events/25817 | http://www.cs.princeton.edu/events/25817 ] Security for All: Modeling Structural Inequities to Design More Secure Systems Users often fall for phishing emails, reuse simple passwords, and fail to effectively utilize "provably" secure systems. These behaviors expose users to significant harm and frustrate industry practitioners and security researchers alike. As consequences of security breaches become ever more grave, it is important to study why humans behave seemingly irrationally. In this talk, I will illustrate how modeling the effects of structural inequities -- variance in skill, socioeconomic status, as well as culture and gender identity -- can both explain apparent irrationality in users’ security behavior and offer tangible improvements in industry systems. Modeling and mitigating security inequities requires a combination of techniques from economic, data scientific, and social science methodologies to develop new tools for systematically understanding and mitigating insecure behavior. Through novel experimental methodology, I empirically show strong evidence of bounded rationality in security behavior: Users make mathematically modelable tradeoffs between the protection offered by security behaviors and the costs of practicing those behaviors, which even in a highly usable system may outweigh the benefits, especially for less resourced users. These findings emphasize the need for industry systems that balance structural inequities and accommodate behavioral variance between users rather than one-size-fits-all security solutions. More broadly, my techniques for modeling and accounting for inequities have offered key insights in growing technical areas beyond security, including algorithmic fairness. Bio: Elissa Redmiles is a Ph.D. Candidate in Computer Science at the University of Maryland and has been a visiting researcher with the Max Planck Institute for Software Systems and the University of Zurich. Elissa’s research interests are broadly in the areas of security and privacy. She uses computational, economic, and social science methods to conduct research on behavioral security. Elissa seeks to understand users’ security and privacy decision-making processes and specifically investigate inequalities that arise in these processes and to mitigate those inequalities through the design of systems that facilitate safety equitably across users. Elissa is the recipient of a NSF Graduate Research Fellowship, a National Science Defense and Engineering Graduate Fellowship, and a Facebook Fellowship. Her work has appeared in popular press publications such as Scientific American, Business Insider, Newsweek, and CNET and has been recognized with the John Karat Usable Privacy and Security Student Research Award, a Distinguished Paper Award at USENIX Security 2018, and a University of Maryland Outstanding Graduate Student Award.

1 0

Sachin Ravi will present his Pre FPO "Meta-Learning for Data and Processing Efficiency" on Tuesday, February 26, 2019 at 9am in CS 301.
by Nicki Mahler 19 Feb '19

19 Feb '19

Sachin Ravi will present his Pre FPO "Meta-Learning for Data and Processing Efficiency" on Tuesday, February 26, 2019 at 9am in CS 301. The members of his committee are as follows: Kai Li (adviser), Jonathan Cohen (PNI), Hugo Larochelle (Google Brain), Jia Deng, and Karthik Narasimhan. All are welcome to attend. Please see below for talk title and abstract. Title : Meta-Learning for Data and Processing Efficiency Abstract : Deep learning models have shown great success in a variety of areas, including computer vision, natural language processing, and reinforcement learning. However, deep learning methods still lack the efficiency and flexibility of human learning. Current deep learning methods involve training on a large amount of data to produce a model that can then specialize to the specific task encoded by the training data. Humans, on the other hand, are constantly learning and mastering new concepts throughout our lives with comparatively little experience. In order to bridge this gap, previous work has suggested the use of meta-learning. Rather than learning how to do a specific task, meta-learning involves learning how-to-learn and utilizing this knowledge to learn new tasks more effectively. This talk focuses on work using meta-learning to improve the data and processing efficiency of deep learning models when learning new tasks. In the first part, we discuss a meta-learning model for the few-shot learning problem, where the aim is to be able to learn a new classification task having unseen classes with few labeled examples. We use a LSTM-based meta-learner model to learn both the initialization and the optimization algorithm used to train another neural network in the few-shot regime and show that our method compares favorably to nearest-neighbor approaches. The second part of the thesis deals with improving the predictive uncertainty of models in the few-shot learning setting. Proper predictive uncertainty is key to deploying machine learning models in the wild (especially in settings with little data), as proper human intervention can be applied when a model’s prediction is known to be uncertain. Using a Bayesian perspective, we propose a meta-learning method which efficiently amortizes hierarchical variational inference across tasks, learning a prior distribution over neural network weights so that a few steps of gradient descent will produce a good task-specific approximate posterior. Finally, we focus on applying meta-learning in the context of making choices that impact processing efficacy. When training a network on multiple tasks, we have a choice between interactive parallelism (training on different tasks one after another) and independent parallelism (using the network to process multiple tasks concurrently). For the simulation environment we consider, we show that there is a trade-off between these two types of processing choices in deep neural networks, where one can learn faster by using interactive parallelism but at the cost of having to do tasks serially. We then discuss a meta-learning algorithm for an agent to learn how to train itself with regard to this trade-off in an environment with unknown serialization cost.

1 0